Automated synchronized navigation system for digital pathology imaging

ABSTRACT

A method for synchronizing navigation in pathology stain images includes (a) downscaling the pathology stain images, (b) estimating rotation of the downscaled images, (c) aligning the downscaled images to generate aligned coordinates, and (d) transforming the aligned coordinates to original image coordinates in the pathology stain images to thereby generate alignment data. Also provided is a system for synchronized navigation in pathology stain images having original resolutions comprising a downscaler, a rotation estimator, an alignment module, and a coordinate transformer. The system may also include an image display system to display corresponding areas of the pathology stain images.

TECHNICAL FIELD

The present disclosure relates to an automated system for synchronizednavigation in digital pathology images, for example, images of tissuesamples stained by different methods.

BACKGROUND

Pathological diagnosis often involves slicing a tissue sample (e.g. abiopsy) into thin slices, placing the slices on individual slides, andstaining the slices with different methods and reagents. For example, atissue sample slice may be stained by hematoxylin and eosin (H&E) stainfor visualizing histological structures of the sample, while an adjacenttissue sample slice may be stained by immunohistochemical (IHC) stainwith a disease-specific antibody. Pathologists commonly perform initialdiagnosis on H&E stained samples and then order IHC staining from thesame biopsy block for validation and prognosis.

With the trend of digitization, specimen slides are often scanned intodigital images (virtual slides) for later viewing on monitors. To make afinal diagnosis, pathologists need to simultaneously examine a region ofinterest on an H&E image and its corresponding area on an IHC image(s)from the same biopsy block. Thus, those stain images need to beaccurately aligned on the monitor(s) and synchronized viewing andnavigation need to be achieved across the images regardless ofmagnification.

To align such stain images is challenging, since there is often a greatdifference in image appearance between two adjacent sample slicesstained by different methods, and various local deformations areinvolved. Adjacent samples are often not related by simpletransformation, and structural changes are unpredictable across adjacentsamples and different magnification. For example, two stain imagesobtained from adjacent but different parts of a tissue block may haveill-defined structural correspondence. The stain images may have alsoweak structures that need to be made explicit in order to align wholeimages. Furthermore, because tissue slices may be stretched or deformedduring sample handling, different parts of each image may transformdifferently from other parts of the same image.

Furthermore, tissue sample placement may also pose challenges foralignment and synchronized navigation in pathology stain images. Forexample, tissue samples may be placed in different orientations and therotation centers of the images are unknown (FIG. 1A). The tissue samplesmay also be placed in different locations on the slides stained bydifferent methods and the images may have very different sizes (FIG.1B).

Existing systems for image alignment and navigation require the user tomanually locate corresponding areas on the virtual slides (images) dueto the problems discussed above. This process has to be redone when theuser navigates away from the aligned regions or at differentresolutions. Those manual adjustments may require zooming in/out andseeking relevant clues with expert knowledge in order to correctlylocate corresponding areas. For very large images (e.g. 100 k×100 k),the manual process is tedious and impractical. In addition, when theimages are examined locally at a high resolution, the appearance betweencorresponding regions diverges rapidly and it becomes difficult to findmatching points.

Therefore, there is a need to develop methods and systems for automatedsynchronized navigation in pathology stain images which are similar inglobal appearance but have local deformations and varied tissue sampleplacements, for example, large images of tissue samples stained bydifferent methods.

SUMMARY

The present disclosure includes an exemplary method for synchronizingnavigation in pathology stain images. Embodiments of the method include(a) downscaling the pathology stain images, (b) estimating rotation ofthe downscaled images, (c) aligning the downscaled images to generatealigned coordinates, and (d) transforming the aligned coordinates tooriginal image coordinates in the pathology stain images having originalresolutions to thereby generate alignment data. Embodiments of themethod may also include displaying corresponding areas of the pathologystain images based on the alignment data.

An exemplary system for automated synchronized navigation in pathologystain images in accordance with the present disclosure comprises adownscaler to detect tissue areas and downsample the pathology stainimages; a rotation estimator to determine rotation angle and rotate thedownscaled images; an alignment module to align the downscaled images togenerate aligned coordinates; and a coordinate transformer to transformthe aligned coordinates to original image coordinates in the pathologystain images to thereby generate alignment data. The exemplary systemfor automated synchronized navigation in pathology stain images may alsocomprise a display system to display corresponding areas of thepathology stain images based on the alignment data.

Also provided is an exemplary computer system for synchronizednavigation in pathology stain images, comprising: one or more processorsconfigured to execute program instructions; and a computer-readablemedium containing executable instructions that, when executed by the oneor more processors, cause the computer system to perform a method forsynchronizing navigation in pathology stain images, the methodcomprising: (a) downscaling the pathology stain images, (b) estimatingrotation of the downscaled images, (c) aligning the downscaled images togenerate aligned coordinates, and (d) transforming the alignedcoordinates to original image coordinates in the pathology stain imagesto thereby generate alignment data. The method may further comprisedisplaying corresponding areas of the pathology stain images based onthe alignment data.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1A shows an example of deviation of tissue orientation, due totissue placement, in three stain images of adjacent tissue samples,stained by H&E (left), IHC with PR antibody (middle), and IHC with HER2antibody (right), respectively. FIG. 1B shows stain images of twoadjacent tissue samples stained by different methods and havingdifferent image size and tissue location, due to tissue placement.

FIG. 2 illustrates a block diagram of an exemplary automatedsynchronized navigation system consistent with the invention.

FIG. 3 shows an exemplary alignment server in an exemplary automatedsynchronized navigation system.

FIG. 4 shows a flow chart illustrating an exemplary method consistentwith the presently-claimed invention.

FIG. 5 shows an example of image pre-processing.

FIG. 6 shows a flow chart illustrating exemplary rotation estimation.

FIG. 7 shows a flow chart illustrating an exemplary coordinatetransformer.

FIG. 8 shows an example of synchronized images (A) (the stain imagesshown are for illustration purposes only and are not actual stain imagesas labeled), and a flow chart illustrating exemplary synchronizeddisplay of multiple pathology stain images (B).

DETAILED DESCRIPTION

Reference will now be made in detail to the exemplary embodiments,examples of which are illustrated in the accompanying drawings. Whereverpossible, the same reference numbers will be used throughout thedrawings to refer to the same or like parts.

The methods and systems disclosed herein have many practicalapplications. For example, exemplary embodiments may be used toautomatically navigate, in a synchronized manner, multiple large imagesof tissue samples stained by different methods. By downscaling andcorrecting tissue placement variations before aligning the images, andthen transforming the aligned coordinates back to the original imagecoordinates, the methods and systems disclosed herein may achieveautomated navigation in different resolutions and bring correspondingareas in the images into synchronized views. The methods and systemsdisclosed herein may be used not only for purposes of pathologicaldiagnosis, but also for synchronized navigation in any images that aresimilar in global appearance but contain local changes or placementvariance, for example, satellite images of the same scene from differentviewpoints.

In the paragraphs that follow, the terms “IHC image” and “H&E image” arefrequently used for illustrative purposes. They are meant to refergenerally to any pathology stain images to be aligned, and not to belimited literally to an IHC or H&E image.

FIG. 2 illustrates a block diagram of an exemplary automatedsynchronized navigation system 200 consistent with the invention. Asshown in FIG. 2, the system may comprise an alignment server (206) and adisplay system (210). Alignment Server 206 may receive at least twostain images (202 and 204) and generate alignment data (208) for thestain images. The Alignment Server may comprise one or more computers,computer systems, programmable processors, or any other devices that maybe used to process large pathology stain images. The Alignment Servermay be implemented as a software program executed in a processor(s)and/or as hardware that performs image alignment based on image content.

Display System 210 may, based on the alignment data, display images ofcorresponding regions of interest from the pathology stain images in asynchronized manner. For example, in some embodiments, when the usermoves the pointer, or curser, of a computer mouse to a point in onestain image and/or signals that the area around the curser is a regionof interest, the Display System may automatically locate thecorresponding areas in the other stain image(s) and display thecorresponding areas.

Display System 210 may comprise one or more display devices. The DisplaySystem may be, for example, one or more computers, personal digitalassistants (PDA), cell phones or smartphones, laptops, desktops, tabletPC, media content players, set-top boxes, television sets, video gamestations/systems, or any electronic device capable of accessing a datanetwork and/or receiving data and display images. In some embodiments,Display System 210 may be, a television(s), monitor(s), projector(s),display panel(s), video game stations/systems, or any other displaydevice(s) capable of providing graphical user interfaces (GUI). In someembodiments, The Display System may comprise one or more computers, orprogrammable processors, etc. for processing and management of thealignment data. In some embodiments, the Display System may comprise asoftware program executed in a processor(s) to allow automatedsynchronized navigation of pathology stain images.

Alignment Server 206 and/or Display System 210 may also comprise adatabase or data management system for storing and retrieving, forexample, image data (202 and 204) and alignment data (208).

Alignment Server 206 and Display System 210 may be operatively connectedto one another via a network or any type of communication links thatallow transmission of data from one component to another, whether wiredor wireless. The network may include Local Area Networks (LANs) and/orWide Area Networks (WANs), and may be wireless, wired, or a combinationthereof.

FIG. 3 shows an exemplary alignment server (300) in an exemplaryautomated synchronized navigation system. As shown in FIG. 3, theexemplary alignment server may comprise a Downscaler (306), a RotationEstimator (308), an Alignment Module (310), and a Coordinate Transformer(312). The various components of the alignment server may be implementedas a software program(s) executed in a processor(s) and/or as hardwarethat performs image processing and/or alignment based on image content.

In some embodiments, Downscaler 306 may be configured to detect tissueareas in the pathology stain images. In some embodiments, Downscaler 306may downscale the pathology stain images to suitable resolutions foraligning the pathology stain images. In some embodiments, RotationEstimator 308 may be configured to estimate rotation angle for an imageagainst a reference image, and rotate the image by that angle. AlignmentModule 310 may be configured to determine correspondent point featuresbetween the downscaled images, create triangular meshes for thedownscaled images from the correspondent point features, and generatealigned coordinates through affine transformation based on thetriangular meshes. Coordinate Transformer 312 may be used to map thealigned coordinates back to original image coordinates at the originalresolutions and in the original orientations. In some embodiments,Coordinate Transformer 312 may generate alignment data from the mappedoriginal image coordinates through affine mapping and triangulations onthe original image coordinates.

FIG. 4 shows a flow chart illustrating an exemplary method that may becarried out by the various components of the Alignment Server. As shownin FIG. 4, at least two pathology stain images may be received whichhave original resolutions (402), for example, an H&E stain image and oneor more IHC images stained by disease-indicating antibodies. The imagesmay be received from an image capturing device(s) or a network or localstorage medium (or media). As exemplified in FIG. 4, in someembodiments, the tissue samples may be positioned in differentorientations and/or different locations in the pathology stain images.The pathology stain images may also have different resolutions.

Thus, in general, the Downscaler may be configured to detect the tissueareas in the stain images (step 404). In some embodiment, the images maybe subjected to pre-processing, including, for example, cropping and/orenhancing, such that the images are suitable for image alignment by theAlignment Module (FIG. 5). Referring back to FIG. 4, in some embodiment,suitable resolutions may be determined based on the memory resource ofthe computer-based alignment server, and the stain images may bedowndsampled (downscaled) to the determined resolutions (step 404).Suitable resolutions may include, but are not limited to, for example,400×400, 800×800, 1200×1200, 1600×1600, 2000×2000, and 2400×2400.

Since the tissue samples may be positioned in different orientations inthe pathology stain images, the Rotation Estimator may be configured, asshown in FIG. 4, to estimate rotation angle at which one of the stainimages may be rotated such that it aligns in general in orientation withanother stain image (“reference image”) (step 406). In some embodiments,an H&E image may serve as a reference image, while the other image(s),for example, an IHC image(s), may be rotated to be aligned with thereference image with respect to orientation.

FIG. 6 shows a flow chart illustrating an exemplary method for rotationestimation. In some embodiments, to determine the rotation angle for animage against a reference image, one or more candidate rotation angles θmay be selected and tested. The selection of the candidate rotationangle θ may be prioritized for efficiency. An exemplary prioritizationorder may be 0°, 180°, ±10°, and so on.

The candidate rotation angle may be verified by rotating the image bythe candidate rotation angle (step 608) and then determining how wellthat image aligns with the reference image after the rotation (steps610-622). The rotation of the image may be followed by background filingand translation compensation to compensate for the tissue placementdeviation caused by the rotation.

FIG. 6 exemplifies an image alignment method for determining matchedpoint features between the rotated image and the reference image.However, other image alignment methods may also be employed, such as theimage alignment methods described in U.S. patent application Ser. No.13/410,960, filed Mar. 2, 2012, which is incorporated herein byreference in its entirety. In principle, the parameters of any alignmentmethod used during rotation estimation may be adjusted to allowexpedient verification of the candidate rotation angle.

As shown in FIG. 6, the images may be partitioned into a plurality ofsub-image windows (step 610), which may be individually processed toextract point features and to match the point features between theimages. Image partitioning may be based on any criteria fit for theimage. For example, the reference image, such as an H&E stain image(602), may be partitioned based on the structural density of the image.In some embodiments, the stain of H&E or other reagent(s) may beseparated before the image is partitioned. In the partitioned signatureimage (612), the sub-image windows may be each centered on a structuralfeature. The size of a sub-image window may be any size that isdesirable for point feature extraction and/or matching determination,for example, 100×100, 200×200, 300×300, 400×400, 500×500, 600×600,800×800, 900×900, or 1000×1000.

Meanwhile, the rotated image, for example, an IHC image, may bepartitioned into correspondent sub-image windows of the same size. Insome embodiments, the IHC image may be partitioned based on direct imagecoordinate correspondence between the IHC and the H&E reference images.

Next, keypoints may be generated for the sub-image windows by analyzingthe content of the sub-image windows (616). Any image processing methodfit for the image may be used to generate keypoints, such as maximumcurvature detection. As exemplified in FIG. 6, keypoints may begenerated for the sub-image windows in the IHC image based on maximumcurvature detection after image segmentation (614).

The keypoints may be cross-matched to the correspondent sub-image windowin the reference image (step 618). In some embodiments, correspondenceof a keypoint in the other image may be determined by cross correlation,for example, normalized cross correlation. The matched keypoints arereferred to as matched point features.

In some embodiments, the matched point features may be filtered toeliminate false matches or outliers (step 620). For example, linesegments connecting matched points between two images may be drawn.Theoretically the lines would be all parallel if the matched points areall true. Thus, a non-parallel line connecting matched points indicatesthat the matching is false and the matched points should be discarded asoutliers.

To verify the candidate rotation angle, the number of matched pointfeatures obtained above may be compared to a predetermined value (step622). If the number of matched point features is greater than thepredetermined value, the candidate rotation angle may be verified as therotation angle for proceeding to the next step. Otherwise a newcandidate rotation angle may be selected and tested as discussed above.The predetermined value may be any integer that achieves rotation angleaccuracy within 10 degrees, for example, 20, 40, 60, 80, 100, 200, 400,1000, or any integer between 1 and 1000. In some embodiments, thepredetermined value may be 40.

In addition, the alignment method may be designed to identify enoughmatched point features to cover at least 20%, 30%, 40%, 50%, 60%, 70%,or 80% coverage of the images. In some embodiments, the parameters ofthe method may be adjusted to achieve at least 40% coverage of theimages.

Next, referring back to FIG. 4, the images that have been processed bythe Downscaler and the Rotation Estimator may be aligned by theAlignment Module to generate aligned coordinates (step 408). In someembodiments, correspondent point features may be determined between theimages. Triangular meshes, for example, Delaunay triangulations, may becreated from the correspondent point features. Aligned coordinateswithin the triangles of correspondent triangle pairs may be generatedthrough affine transformation based on the triangular meshes. In someembodiments, the correspondent point features between the images may befurther refined based on affine transformation estimation using thetriangular meshes. Refined triangular meshes may be created for theimages from the refined correspondent point features. And alignedcoordinates may generated through affine transformation based on refinedtriangular meshes. Detailed descriptions of exemplary methods of imagealignment and working examples may be found in U.S. patent applicationSer. No. 13/410,960. Other algorithms or methods suitable forautomatically generating aligned coordinates for digital pathologyimages may also be employed by the Alignment Module.

In some circumstances, the number of aligned coordinates between twoimages may be so large as to render the performance of the systemundesirable. Thus, in some embodiments, the number of alignedcoordinates may be controlled. For example, if more than a predeterminednumber of aligned coordinates are generated, the alignment module mayadjust the parameter(s) and re-align the images using the adjustedparameters. The predetermined number may be, for example, determined bythe system implementing the method. For example, the predeterminednumber may be 1000, 2000, 3000, 4000, 5000, or any integer in betweenthose numbers.

Next, since rotation estimation and image alignment may be carried outwith downscaled images, the aligned coordinates may need to be mappedback to the original images at the maximum resolution such that theautomated navigation system described herein can scale alignmentinformation on the fly for the image viewer(s) or monitor(s). Referringback to FIG. 4, Coordinate Transformer may convert the alignedcoordinates back to the original image coordinates of the images at theoriginal resolutions and in the original orientations (step 410). Theresulting alignment data (412) are then passed on to the Display Systemfor image display and user-initiated image navigation.

FIG. 7 shows a flow chart illustrating an exemplary CoordinateTransformer 700. Based on the parameters used for the earlier step ofdownscaling (downsampling), as well as the rotation angle at which theimage(s) was rotated with respect to the reference image, CoordinateTransformer 700 may inversely scale and rotate the aligned coordinates(702), generated by the Alignment Module, back to original coordinatesin the images with respect to the original orientations and the originalresolutions (step 704). The resulting original coordinates may be usedto recalculate triangular meshes for respective images (step 706). Insome embodiments, the triangular meshes may be Delaunay triangulations.The Coordinate Transformer may, through affine mapping, determinematched points within the triangular meshes (step 708). For example,coordinates within the areas of pairs of correspondent triangles in thetriangular meshes may be matched through interpolation of affinetransformation on the pairs of correspondent triangles. The CoordinateTransformer may output alignment data 710, which include, but are notlimited to, matched points between the images, triangles for therespective images, mapping functions for the respective images, androtation angle(s) of the images.

In some embodiments, the alignment data may be stored in the AlignmentServer. The Display System may obtain the alignment data from theAlignment Server for synchronized navigation and display of thepathology stain images. In some embodiments, the alignment data, oncegenerated by the Coordinate Transformer, may be passed on to the DisplaySystem for storage. In general, the alignment data may be storedtogether with the original images for expedient retrieval and display.

Display System may comprise one or more monitors or image displaydevices for the user to navigate multiple images simultaneously. Forexample, the user may navigate to and focus on a region of interest inone stain image, and simultaneously view the corresponding regions inthe other stain images at such resolutions to allow detailed analysis.FIG. 8A illustrates how four pathology stain images, including an H&Eimage and three IHC images stained for estrogen receptor (ER), HER-2,and progesterone receptor (PR), respectively, may be synchronized on anmonitor(s). As exemplified in FIG. 8A, the user may use the H&E image asa reference image to see the structural features of a region of interestin the tissue sample, and in the mean time, view the correspondingregions in the IHC images to analyze presence and distribution ofspecific antigens.

FIG. 8B. shows a flow chart illustrating exemplary synchronization ofmultiple stain images. The user may manipulate a stain image in anymanner. For example, the user may manipulate a stain image by moving thecursor of, for example, a mouse or any other pointing device, in thatstain image such that a certain area of the image is displayed. In someembodiments, the user may specify the point of the cursor to be thecenter of the image area to display. In some embodiments, the user mayspecify an area in the image to focus on and display.

As shown in FIG. 8, once Display System 210 determines the centerposition (point) of the manipulated, or displayed, image area (802),that point may be mapped to a triangle of the same image as stored inthe alignment data described herein (step 804). The stored triangles ofthe image may be iterated to determine if center position 802 fallswithin the triangle until the correct triangle is identified. Once thetriangle containing center position 802 is determined, the point istransformed to points in the other images through affine transformationsof the triangle (step 806), which are stored in the alignment data.

In some embodiments, triangle mapping and point transformation arecarried out by Display System. In some embodiments, those functions maybe carried out by another component of the navigation system.

The Display System then moves the center position(s) of the otherimage(s) to the transformed points (808) and display the images. Sincethe Alignment Server pre-computes the alignment data, which is storedbefore images are navigated and displayed, the Display System mayquickly access the data, determine correspondent regions of interest,and display them in an automated and synchronized manner.

As shown in FIG. 8A, more than two images may be synchronized by thedisclosed embodiments. In some embodiments, one of the images, forexample, an H&E image, may serve as a reference image. The center of themanipulated image on display, for example, an ER IHC image, may betransformed to a point in the H&E image through stored affinetransformations, and the transformed point in the H&E image may betransformed again to a point to another IHC image, e.g. PR IHC image,through stored affine matrices. Thus, in some embodiments, multipleimages may be synchronized through a reference image. When a new stainimage is provided to the automated navigation system, the AlignmentServer may compute alignment data only for the new stain image againstthe reference image, and store the alignment data together with thealignment data generated for other existing images.

Pathology images, including IHC and H&E images, are merely exemplaryimages. Any types of images consistent with disclosed embodiments mayalso be candidates for automated and synchronized navigation using themethods and systems disclosed herein with modifications and changeswithout departing from the broader spirit and scope of the invention.

It is understood that the above-described exemplary process flows inFIGS. 2-4 and 6-8 are for illustrative purposes only. Certain steps maybe deleted, combined, or rearranged, and additional steps may be added.

The methods disclosed herein may be implemented as a computer programproduct, i.e., a computer program tangibly embodied in a non-transitoryinformation carrier, e.g., in a machine-readable storage device, or atangible non-transitory computer-readable medium, for execution by, orto control the operation of, data processing apparatus, e.g., aprogrammable processor, multiple processors, a computer, or multiplecomputers. A computer program may be written in any appropriate form ofprogramming language, including compiled or interpreted languages, andit may be deployed in various forms, including as a standalone programor as a module, component, subroutine, or other unit suitable for use ina computing environment. A computer program may be deployed to beexecuted on one computer or on multiple computers at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

A portion or all of the methods disclosed herein may also be implementedby an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), a printed circuit board (PCB), a digital signal processor(DSP), a combination of programmable logic components and programmableinterconnects, a single central processing unit (CPU) chip, a CPU chipcombined on a motherboard, a general purpose computer, or any othercombination of devices or modules capable of performing automatic imagenavigation disclosed herein.

In the preceding specification, the invention has been described withreference to specific exemplary embodiments. It will, however, beevident that various modifications and changes may be made withoutdeparting from the broader spirit and scope of the invention as setforth in the claims that follow. The specification and drawings areaccordingly to be regarded as illustrative rather than restrictive.Other embodiments of the invention may be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein.

What is claimed is:
 1. A computer-implemented method for synchronizingnavigation in pathology stain images, the method comprising the stepsperformed by one or more computers of: (a) downscaling the pathologystain images; (b) estimating rotation of the downscaled images; (c)aligning the downscaled images to generate aligned coordinates; and (d)transforming the aligned coordinates to original image coordinates inthe pathology stain images to thereby generate alignment data.
 2. Themethod of claim 1, wherein (a) downscaling the pathology stain imagescomprises: detecting tissue areas in the pathology stain images;determining suitable resolutions for aligning the pathology stainimages; and downscaling the pathology stain images to the determinedresolutions.
 3. The method of claim 1, wherein (b) estimating rotationcomprises: selecting a candidate rotation angle; rotating one of thedownscaled images by the candidate rotation angle; aligning, after therotating, the downscaled images to determine matched point featuresbetween the downscaled images; and selecting a final rotation anglebased on the number of matched point features.
 4. The method of claim 3,wherein estimating rotation further comprises filtering out falsematched point features.
 5. The method of claim 3, further comprising:filling in background for the rotated image; and compensatingtranslation for the rotated image.
 6. The method of claim 1, wherein (c)aligning the downscaled images comprises: determining correspondentpoint features between the downscaled images; creating triangular meshesfor the downscaled images from the correspondent point features;refining point correspondence between the downscaled images based onaffine transformation estimation using the triangular meshes; creatingrefined triangular meshes for the downscaled images from the refinedpoint correspondence; and generating aligned coordinates through affinetransformation based on the refined triangular meshes.
 7. The method ofclaim 6, further comprising re-determining aligned coordinates betweenthe downscaled images to control the number of aligned coordinates. 8.The method of claim 1, wherein (d) transforming the aligned coordinatesto original image coordinates comprises: mapping the aligned coordinatesback to original image coordinates in the pathology stain images atoriginal resolutions and in original orientations; creating triangularmeshes for the pathology stain images based on the mapped original imagecoordinates; and generating alignment data through affine mapping usingthe triangular meshes.
 9. The method of claim 8, wherein generatingalignment data comprises generating forward and backward mappingfunctions between the pathology stain images.
 10. The method of claim 1,wherein the pathology stain images are selected from immunohistochemical(IHC) stain images and hematoxylin and eosin (H&E) stain images.
 11. Themethod of claim 1, further comprising displaying corresponding areas ofthe pathology stain images based on the alignment data.
 12. The methodof claim 11, wherein the displaying comprises: determining the centerposition of one of the pathology stain images; transforming the centerposition to a corresponding coordinate another pathology stain imagebased on the alignment data; and moving the center position of theanother pathology stain image to the corresponding coordinate.
 13. Acomputer-implemented system for synchronized navigation in pathologystain images, comprising: a downscaler to detect tissue areas anddownsample the pathology stain images; a rotation estimator to determinea rotation angle and rotate the downscaled images; an alignment moduleto align the downscaled images to generate aligned coordinates; and acoordinate transformer to transform the aligned coordinates to originalimage coordinates in the pathology stain images to thereby generatealignment data.
 14. The system of claim 13, further comprising a displaysystem to display corresponding areas of the pathology stain imagesbased on the alignment data.
 15. The system of claim 13, furthercomprising a database to store alignment data.
 16. The system of claim13, wherein the downscaler is configured to: detect tissue areas in thepathology stain images; determine suitable resolutions for aligning thepathology stain images; and downscale the pathology stain images to thedetermined resolutions.
 17. The system of claim 13, wherein the rotationestimator is configured to: estimate the rotation angle between thedownscaled images; rotate at least one of the downscaled images by therotation angle such that the downscaled images are aligned inorientation; fill in background for the at least one rotated image; andcompensate translation for the at least one rotated image
 18. The systemof claim 13, wherein the alignment module is configured to: determinecorrespondent point features between the downscaled images; createtriangular meshes for the downscaled images from the correspondent pointfeatures; and generate aligned coordinates through affine transformationbased on the triangular meshes.
 19. The system of claim 13, wherein thecoordinate transformer is configured to: map the aligned coordinatesback to original image coordinates in the pathology stain images atoriginal resolutions and original orientations; create triangular meshesfor the pathology stain images based on the mapped original imagecoordinates; and generate alignment data through affine mapping usingthe triangular meshes.
 20. The system of claim 14, wherein the system isconfigured to: identify the center position of a first one of thepathology stain images; transform the center position to a correspondingcoordinate in a second one of the pathology stain images based on thealignment data; and move the center position of the another pathologystain image to the corresponding coordinate.